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A rough set approach for the discovery of classification rules in interval-valued information systems

Yee Leung, Manfred Fischer, Wei-Zhi Wu and Ju-Sheng Mi

MPRA Paper from University Library of Munich, Germany

Abstract: A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects optimal decision rules with a minimal set of features necessary and sufficient for classification of real-valued data. A rough set knowledge discovery framework is formulated for the analysis of interval-valued information systems converted from real-valued raw decision tables. The optimal feature selection method for information systems with interval-valued features obtains all classification rules hidden in a system through a knowledge induction process. Numerical examples are employed to substantiate the conceptual arguments.

Keywords: Classification; Interval-valued information systems; Knowledge discovery; Knowledge reduction; Rough sets (search for similar items in EconPapers)
JEL-codes: C38 (search for similar items in EconPapers)
Date: 2008
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Published in International Journal of Approximate Reasoning 2.47(2008): pp. 233-246

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